{
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  "metadata": {
    "colab": {
      "name": "functional.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "colab_type": "text",
        "id": "-zMKQx6DkKwt"
      },
      "cell_type": "markdown",
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "J307vsiDkMMW",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "vCMYwDIE9dTT"
      },
      "cell_type": "markdown",
      "source": [
        "# The Keras Functional API in TensorFlow"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "lAJfkZ-K9flj"
      },
      "cell_type": "markdown",
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/alpha/guide/keras/functional\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r2/guide/keras/functional.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/r2/guide/keras/functional.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "ITh3wzORxgpw"
      },
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "8ZxTfanfo64p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "131bc202-7a81-4187-9380-59a4d02e8af3"
      },
      "cell_type": "code",
      "source": [
        "!pip install pydot\n",
        "!apt-get install graphviz"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pydot in /usr/local/lib/python3.6/dist-packages (1.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.1.4 in /usr/local/lib/python3.6/dist-packages (from pydot) (2.3.1)\n",
            "Reading package lists... Done\n",
            "Building dependency tree\n",
            "Reading state information... Done\n",
            "graphviz is already the newest version (2.40.1-2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 10 not upgraded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "HFbM9dcfxh4l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "82881dde-6a76-4671-e99f-5e57cfe39944"
      },
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "!pip install tensorflow-gpu==2.0.0-alpha0\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.keras.backend.clear_session()  # For easy reset of notebook state."
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "ZI47-lpfkZ5c"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Introduction\n",
        "\n",
        "You're already familiar with the use of `keras.Sequential()` to create models.\n",
        "The Functional API is a way to create models that is more flexible than `Sequential`:\n",
        "it can handle models with non-linear topology, models with shared layers,\n",
        "and models with multiple inputs or outputs.\n",
        "\n",
        "It's based on the idea that a deep learning model\n",
        "is usually a directed acyclic graph (DAG) of layers.\n",
        "The Functional API a set of tools for **building graphs of layers**.\n",
        "\n",
        "Consider the following model:\n",
        "\n",
        "```\n",
        "(input: 784-dimensional vectors)\n",
        "       ↧\n",
        "[Dense (64 units, relu activation)]\n",
        "       ↧\n",
        "[Dense (64 units, relu activation)]\n",
        "       ↧\n",
        "[Dense (10 units, softmax activation)]\n",
        "       ↧\n",
        "(output: probability distribution over 10 classes)\n",
        "```\n",
        "\n",
        "It's a simple graph of 3 layers.\n",
        "\n",
        "To build this model with the functional API,\n",
        "you would start by creating an input node:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Yxi0LaSHkDT-",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "inputs = keras.Input(shape=(784,))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Mr3Z_Pxcnf-H"
      },
      "cell_type": "markdown",
      "source": [
        "Here we just specify the shape of our data: 784-dimensional vectors.\n",
        "None that the batch size is always omitted, we only specify the shape of each sample.\n",
        "For an input meant for images of shape `(32, 32, 3)`, we would have used:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "0-2Q2nJNneIO",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "img_inputs = keras.Input(shape=(32, 32, 3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "HoMFNu-pnkgF"
      },
      "cell_type": "markdown",
      "source": [
        "What gets returned, `inputs`, contains information about the shape and dtype of the\n",
        "input data that you expect to feed to your model:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ddIr9LPJnibj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f212bf52-b14a-438b-ff4c-29d526ddaa73"
      },
      "cell_type": "code",
      "source": [
        "inputs.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([None, 784])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "lZkLJeQonmTe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "62301b13-d52b-4610-da6e-003b337ccaed"
      },
      "cell_type": "code",
      "source": [
        "inputs.dtype"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tf.float32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "kZnhhndTnrzC"
      },
      "cell_type": "markdown",
      "source": [
        "You create a new node in the graph of layers by calling a layer on this `inputs` object:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "sMyyMTqDnpYV",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers\n",
        "\n",
        "dense = layers.Dense(64, activation='relu')\n",
        "x = dense(inputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "besm-lgFnveV"
      },
      "cell_type": "markdown",
      "source": [
        "The \"layer call\" action is like drawing an arrow from \"inputs\" to this layer we created.\n",
        "We're \"passing\" the inputs to the `dense` layer, and out we get `x`.\n",
        "\n",
        "Let's add a few more layers to our graph of layers:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "DbF-MIO2ntf7",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = layers.Dense(64, activation='relu')(x)\n",
        "outputs = layers.Dense(10, activation='softmax')(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "B38UlEIlnz_8"
      },
      "cell_type": "markdown",
      "source": [
        "At this point, we can create a `Model` by specifying its inputs and outputs in the graph of layers:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "MrSfwvl-nx9s",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = keras.Model(inputs=inputs, outputs=outputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "5EeeV1xJn3jW"
      },
      "cell_type": "markdown",
      "source": [
        "To recap, here is our full model definition process:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "xkz7oqj2n1-q",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(784,), name='img')\n",
        "x = layers.Dense(64, activation='relu')(inputs)\n",
        "x = layers.Dense(64, activation='relu')(x)\n",
        "outputs = layers.Dense(10, activation='softmax')(x)\n",
        "\n",
        "model = keras.Model(inputs=inputs, outputs=outputs, name='mnist_model')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "jJzocCbdn6qj"
      },
      "cell_type": "markdown",
      "source": [
        "Let's check out what the model summary looks like:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "GirC9odQn5Ep",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "f078771a-a156-4897-ea80-c42414bb0b7d"
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mnist_model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #\n",
            "=================================================================\n",
            "img (InputLayer)             [(None, 784)]             0\n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 64)                50240\n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                4160\n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                650\n",
            "=================================================================\n",
            "Total params: 55,050\n",
            "Trainable params: 55,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "mbNqYAlOn-vA"
      },
      "cell_type": "markdown",
      "source": [
        "We can also plot the model as a graph:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "JYh2wLain8Oi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "b5d70f34-a34c-401d-b5da-0456ba1e905b"
      },
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model, 'my_first_model.png')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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r5s+fz2Qym5qaxrrhEOgIQUFBMCGbacZBr1wuFwD6+/tt1tK+c3JyLM8MNn++\nPIR58+adOXNGrVanpqYWFxcfOHDA4VBDMJlMJpOJw+GMdcMh0C8Oio6OhgnZTDMOeg0ODmYwGNXV\n1TZrpVIpl8sd67MntVpdX18PAGKxeN++fQsXLqyvr3csFFj9Wpi+BlmyZMlY41jS3t6ek5MjkUg2\nbNgAE6OZ9nDQq1gsjouLKy0tPX78eHd3961bt/Lz8821XC43OTm5sLAwLy+vu7vbaDSqVKqHDx8O\nH1OtVm/atKmhoWFgYKC2tratrS0sLMyxUADw3XffFRUV/fDDDwaDoaamJiUlRSaTvffee3RtZWXl\niPc5FEX19vaaTCaKojo7O4uLi5cuXcpkMk+fPk33rxOhmcNlP4RR3uf09PSkpKR4e3t7enpGRESk\np6cDgEQiuXnzJkVR/f39qampMpnMw8OD/ieoq6vLzc0lSRIAZs+e3dLSkp+fT39A/v7+TU1Nra2t\n4eHhU6ZMYTKZ06dPT0tLGxwctBdqxPTef//9wMBAPp/v4eEhkUjeeecdtVptrj179qxAINizZ4/1\nhuXl5SEhISRJstlsBoMB/33kFBoampmZ+fjxY8uV3d5Me9fDNsa/lpSUxMfHW5djJiD0+BzrwVQo\nPx+ezDyTXhsaGgj7KJVKdyfofp7J99QGBQXhbmJ4nsnjFTMi2CuaYK9ogr2iCfaKJtgrmmCvaIK9\nogn2iibYK5pgr2iCvaIJ9oom2Cua2P2eDr339CLJ5cuXw8LCrMttHK9SqVShUIx/ShgnEBYWZvNH\nlpPi/f6TENy/ogn2iibYK5pgr2jyf/VIZ1gTaKc+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "QtgX2RoGoDZo"
      },
      "cell_type": "markdown",
      "source": [
        "And optionally display the input and output shapes of each layer in the plotted graph:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "7FGesSSuoAG5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "9ce8e0f7-6113-4189-aef2-8157b4f56ecd"
      },
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model, 'my_first_model_with_shape_info.png', show_shapes=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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69ddfVYqxJ8yz1jV8n8OLTavzrN3d3SEWi9GnTx/MmjULAODg4IC+fftCLBbLZz08WxYx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\nCHCyZowxEeBkzRhjIvD/9tDVP0H/WWIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "PBZ9XE6LoWvi"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "This figure and the code we wrote are virtually identical. In the code version,\n",
        "the connection arrows are simply replaced by the call operation.\n",
        "\n",
        "A \"graph of layers\" is a very intuitive mental image for a deep learning model,\n",
        "and the functional API is a way to create models that closely mirrors this mental image."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "WUUHMaKLoZDn"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "## Training, evaluation, and inference\n",
        "\n",
        "Training, evaluation, and inference work exactly in the same way for models built\n",
        "using the Functional API as for Sequential models.\n",
        "\n",
        "Here is a quick demonstration.\n",
        "\n",
        "Here we load MNIST image data, reshape it into vectors,\n",
        "fit the model on the data (while monitoring performance on a validation split),\n",
        "and finally we evaluate our model on the test data:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "DnHvkD22oFEY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "68c1e96f-7531-460d-ed4e-6845126d581b"
      },
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
        "x_train = x_train.reshape(60000, 784).astype('float32') / 255\n",
        "x_test = x_test.reshape(10000, 784).astype('float32') / 255\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=keras.optimizers.RMSprop(),\n",
        "              metrics=['accuracy'])\n",
        "history = model.fit(x_train, y_train,\n",
        "                    batch_size=64,\n",
        "                    epochs=5,\n",
        "                    validation_split=0.2)\n",
        "test_scores = model.evaluate(x_test, y_test, verbose=0)\n",
        "print('Test loss:', test_scores[0])\n",
        "print('Test accuracy:', test_scores[1])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/5\n",
            "48000/48000 [==============================] - 3s 64us/sample - loss: 0.3414 - accuracy: 0.9016 - val_loss: 0.1719 - val_accuracy: 0.9501\n",
            "Epoch 2/5\n",
            "48000/48000 [==============================] - 3s 57us/sample - loss: 0.1568 - accuracy: 0.9526 - val_loss: 0.1365 - val_accuracy: 0.9605\n",
            "Epoch 3/5\n",
            "48000/48000 [==============================] - 3s 58us/sample - loss: 0.1144 - accuracy: 0.9660 - val_loss: 0.1262 - val_accuracy: 0.9625\n",
            "Epoch 4/5\n",
            "48000/48000 [==============================] - 3s 54us/sample - loss: 0.0929 - accuracy: 0.9716 - val_loss: 0.1100 - val_accuracy: 0.9701\n",
            "Epoch 5/5\n",
            "48000/48000 [==============================] - 3s 55us/sample - loss: 0.0759 - accuracy: 0.9770 - val_loss: 0.1139 - val_accuracy: 0.9670\n",
            "Test loss: 0.100577776569454\n",
            "Test accuracy: 0.9696\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "c3nq2fjiLCkE"
      },
      "cell_type": "markdown",
      "source": [
        "For a complete guide about model training and evaluation, see [Guide to Training & Evaluation](./training_and_evaluation.ipynb)."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "XOsL56zDorLh"
      },
      "cell_type": "markdown",
      "source": [
        "## Saving and serialization\n",
        "\n",
        "Saving and serialization work exactly in the same way for models built\n",
        "using the Functional API as for Sequential models.\n",
        "\n",
        "To standard way to save a Functional model is to call `model.save()` to save the whole model into a single file.\n",
        "You can later recreate the same model from this file, even if you no longer have access to the code\n",
        "that created the model.\n",
        "\n",
        "This file includes:\n",
        "- The model's architecture\n",
        "- The model's weight values (which were learned during training)\n",
        "- The model's training config (what you passed to `compile`), if any\n",
        "- The optimizer and its state, if any (this enables you to restart training where you left off)"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "kN-AO7xvobtr",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.save('path_to_my_model.h5')\n",
        "del model\n",
        "# Recreate the exact same model purely from the file:\n",
        "model = keras.models.load_model('path_to_my_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "u0J0tFPHK4pb"
      },
      "cell_type": "markdown",
      "source": [
        "For a complete guide about model saving, see [Guide to Saving and Serializing Models](./saving_and_serializing.ipynb)."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "lKz1WWr2LUzF"
      },
      "cell_type": "markdown",
      "source": [
        "## Using the same graph of layers to define multiple models\n",
        "\n",
        "\n",
        "In the functional API, models are created by specifying their inputs\n",
        "and outputs in a graph of layers. That means that a single graph of layers\n",
        "can be used to generate multiple models.\n",
        "\n",
        "In the example below, we use the same stack of layers to instantiate two models:\n",
        "an `encoder` model that turns image inputs into 16-dimensional vectors,\n",
        "and an end-to-end `autoencoder` model for training.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "WItZQr6LuVbF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "outputId": "f1323866-dfc7-4391-c6f2-b3d21e72d59b"
      },
      "cell_type": "code",
      "source": [
        "encoder_input = keras.Input(shape=(28, 28, 1), name='img')\n",
        "x = layers.Conv2D(16, 3, activation='relu')(encoder_input)\n",
        "x = layers.Conv2D(32, 3, activation='relu')(x)\n",
        "x = layers.MaxPooling2D(3)(x)\n",
        "x = layers.Conv2D(32, 3, activation='relu')(x)\n",
        "x = layers.Conv2D(16, 3, activation='relu')(x)\n",
        "encoder_output = layers.GlobalMaxPooling2D()(x)\n",
        "\n",
        "encoder = keras.Model(encoder_input, encoder_output, name='encoder')\n",
        "encoder.summary()\n",
        "\n",
        "x = layers.Reshape((4, 4, 1))(encoder_output)\n",
        "x = layers.Conv2DTranspose(16, 3, activation='relu')(x)\n",
        "x = layers.Conv2DTranspose(32, 3, activation='relu')(x)\n",
        "x = layers.UpSampling2D(3)(x)\n",
        "x = layers.Conv2DTranspose(16, 3, activation='relu')(x)\n",
        "decoder_output = layers.Conv2DTranspose(1, 3, activation='relu')(x)\n",
        "\n",
        "autoencoder = keras.Model(encoder_input, decoder_output, name='autoencoder')\n",
        "autoencoder.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"encoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #\n",
            "=================================================================\n",
            "img (InputLayer)             [(None, 28, 28, 1)]       0\n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 26, 26, 16)        160\n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 32)        4640\n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 8, 8, 32)          0\n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 6, 6, 32)          9248\n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 4, 4, 16)          4624\n",
            "_________________________________________________________________\n",
            "global_max_pooling2d (Global (None, 16)                0\n",
            "=================================================================\n",
            "Total params: 18,672\n",
            "Trainable params: 18,672\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"autoencoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #\n",
            "=================================================================\n",
            "img (InputLayer)             [(None, 28, 28, 1)]       0\n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 26, 26, 16)        160\n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 32)        4640\n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 8, 8, 32)          0\n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 6, 6, 32)          9248\n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 4, 4, 16)          4624\n",
            "_________________________________________________________________\n",
            "global_max_pooling2d (Global (None, 16)                0\n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 4, 4, 1)           0\n",
            "_________________________________________________________________\n",
            "conv2d_transpose (Conv2DTran (None, 6, 6, 16)          160\n",
            "_________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTr (None, 8, 8, 32)          4640\n",
            "_________________________________________________________________\n",
            "up_sampling2d (UpSampling2D) (None, 24, 24, 32)        0\n",
            "_________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTr (None, 26, 26, 16)        4624\n",
            "_________________________________________________________________\n",
            "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         145\n",
            "=================================================================\n",
            "Total params: 28,241\n",
            "Trainable params: 28,241\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "oNeg3WWFuYZK"
      },
      "cell_type": "markdown",
      "source": [
        "Note that we make the decoding architecture strictly symmetrical to the encoding architecture,\n",
        "so that we get an output shape that is the same as the input shape `(28, 28, 1)`.\n",
        "The reverse of a `Conv2D` layer is a `Conv2DTranspose` layer, and the reverse of a `MaxPooling2D`\n",
        "layer is an `UpSampling2D` layer."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "h1FVW4j-uc6Y"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "## All models are callable, just like layers\n",
        "\n",
        "You can treat any model as if it were a layer, by calling it on an `Input` or on the output of another layer.\n",
        "Note that by calling a model you aren't just reusing the architecture of the model, you're also reusing its weights.\n",
        "\n",
        "Let's see this in action. Here's a different take on the autoencoder example that creates an encoder model, a decoder model,\n",
        "and chain them in two calls to obtain the autoencoder model:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Ld7KdsQ_uZbr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1003
        },
        "outputId": "aaf1cf68-491a-4876-8221-44cc9f34ec00"
      },
      "cell_type": "code",
      "source": [
        "encoder_input = keras.Input(shape=(28, 28, 1), name='original_img')\n",
        "x = layers.Conv2D(16, 3, activation='relu')(encoder_input)\n",
        "x = layers.Conv2D(32, 3, activation='relu')(x)\n",
        "x = layers.MaxPooling2D(3)(x)\n",
        "x = layers.Conv2D(32, 3, activation='relu')(x)\n",
        "x = layers.Conv2D(16, 3, activation='relu')(x)\n",
        "encoder_output = layers.GlobalMaxPooling2D()(x)\n",
        "\n",
        "encoder = keras.Model(encoder_input, encoder_output, name='encoder')\n",
        "encoder.summary()\n",
        "\n",
        "decoder_input = keras.Input(shape=(16,), name='encoded_img')\n",
        "x = layers.Reshape((4, 4, 1))(decoder_input)\n",
        "x = layers.Conv2DTranspose(16, 3, activation='relu')(x)\n",
        "x = layers.Conv2DTranspose(32, 3, activation='relu')(x)\n",
        "x = layers.UpSampling2D(3)(x)\n",
        "x = layers.Conv2DTranspose(16, 3, activation='relu')(x)\n",
        "decoder_output = layers.Conv2DTranspose(1, 3, activation='relu')(x)\n",
        "\n",
        "decoder = keras.Model(decoder_input, decoder_output, name='decoder')\n",
        "decoder.summary()\n",
        "\n",
        "autoencoder_input = keras.Input(shape=(28, 28, 1), name='img')\n",
        "encoded_img = encoder(autoencoder_input)\n",
        "decoded_img = decoder(encoded_img)\n",
        "autoencoder = keras.Model(autoencoder_input, decoded_img, name='autoencoder')\n",
        "autoencoder.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"encoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #\n",
            "=================================================================\n",
            "original_img (InputLayer)    [(None, 28, 28, 1)]       0\n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 16)        160\n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 24, 24, 32)        4640\n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 32)          0\n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 6, 6, 32)          9248\n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 4, 4, 16)          4624\n",
            "_________________________________________________________________\n",
            "global_max_pooling2d_1 (Glob (None, 16)                0\n",
            "=================================================================\n",
            "Total params: 18,672\n",
            "Trainable params: 18,672\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"decoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #\n",
            "=================================================================\n",
            "encoded_img (InputLayer)     [(None, 16)]              0\n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 4, 4, 1)           0\n",
            "_________________________________________________________________\n",
            "conv2d_transpose_4 (Conv2DTr (None, 6, 6, 16)          160\n",
            "_________________________________________________________________\n",
            "conv2d_transpose_5 (Conv2DTr (None, 8, 8, 32)          4640\n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 24, 24, 32)        0\n",
            "_________________________________________________________________\n",
            "conv2d_transpose_6 (Conv2DTr (None, 26, 26, 16)        4624\n",
            "_________________________________________________________________\n",
            "conv2d_transpose_7 (Conv2DTr (None, 28, 28, 1)         145\n",
            "=================================================================\n",
            "Total params: 9,569\n",
            "Trainable params: 9,569\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"autoencoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #\n",
            "=================================================================\n",
            "img (InputLayer)             [(None, 28, 28, 1)]       0\n",
            "_________________________________________________________________\n",
            "encoder (Model)              (None, 16)                18672\n",
            "_________________________________________________________________\n",
            "decoder (Model)              (None, 28, 28, 1)         9569\n",
            "=================================================================\n",
            "Total params: 28,241\n",
            "Trainable params: 28,241\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "icQFny_huiXC"
      },
      "cell_type": "markdown",
      "source": [
        "As you can see, model can be nested: a model can contain submodels (since a model is just like a layer).\n",
        "\n",
        "A common use case for model nesting is *ensembling*.\n",
        "As an example, here's how to ensemble a set of models into a single model that averages their predictions:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ZBlZbRn5uk-9",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_model():\n",
        "  inputs = keras.Input(shape=(128,))\n",
        "  outputs = layers.Dense(1, activation='sigmoid')(inputs)\n",
        "  return keras.Model(inputs, outputs)\n",
        "\n",
        "model1 = get_model()\n",
        "model2 = get_model()\n",
        "model3 = get_model()\n",
        "\n",
        "inputs = keras.Input(shape=(128,))\n",
        "y1 = model1(inputs)\n",
        "y2 = model2(inputs)\n",
        "y3 = model3(inputs)\n",
        "outputs = layers.average([y1, y2, y3])\n",
        "ensemble_model = keras.Model(inputs=inputs, outputs=outputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "e1za1TZxuoId"
      },
      "cell_type": "markdown",
      "source": [
        "## Manipulating complex graph topologies\n",
        "\n",
        "\n",
        "### Models with multiple inputs and outputs\n",
        "\n",
        "The functional API makes it easy to manipulate multiple inputs and outputs.\n",
        "This cannot be handled with the Sequential API.\n",
        "\n",
        "Here's a simple example.\n",
        "\n",
        "Let's say you're building a system for ranking custom issue tickets by priority and routing them to the right department.\n",
        "\n",
        "You model will have 3 inputs:\n",
        "\n",
        "- Title of the ticket (text input)\n",
        "- Text body of the ticket (text input)\n",
        "- Any tags added by the user (categorical input)\n",
        "\n",
        "It will have two outputs:\n",
        "\n",
        "- Priority score between 0 and 1 (scalar sigmoid output)\n",
        "- The department that should handle the ticket (softmax output over the set of departments)\n",
        "\n",
        "Let's built this model in a few lines with the Functional API."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Gt91OtzbutJy",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_tags = 12  # Number of unique issue tags\n",
        "num_words = 10000  # Size of vocabulary obtained when preprocessing text data\n",
        "num_departments = 4  # Number of departments for predictions\n",
        "\n",
        "title_input = keras.Input(shape=(None,), name='title')  # Variable-length sequence of ints\n",
        "body_input = keras.Input(shape=(None,), name='body')  # Variable-length sequence of ints\n",
        "tags_input = keras.Input(shape=(num_tags,), name='tags')  # Binary vectors of size `num_tags`\n",
        "\n",
        "# Embed each word in the title into a 64-dimensional vector\n",
        "title_features = layers.Embedding(num_words, 64)(title_input)\n",
        "# Embed each word in the text into a 64-dimensional vector\n",
        "body_features = layers.Embedding(num_words, 64)(body_input)\n",
        "\n",
        "# Reduce sequence of embedded words in the title into a single 128-dimensional vector\n",
        "title_features = layers.LSTM(128)(title_features)\n",
        "# Reduce sequence of embedded words in the body into a single 32-dimensional vector\n",
        "body_features = layers.LSTM(32)(body_features)\n",
        "\n",
        "# Merge all available features into a single large vector via concatenation\n",
        "x = layers.concatenate([title_features, body_features, tags_input])\n",
        "\n",
        "# Stick a logistic regression for priority prediction on top of the features\n",
        "priority_pred = layers.Dense(1, activation='sigmoid', name='priority')(x)\n",
        "# Stick a department classifier on top of the features\n",
        "department_pred = layers.Dense(num_departments, activation='softmax', name='department')(x)\n",
        "\n",
        "# Instantiate an end-to-end model predicting both priority and department\n",
        "model = keras.Model(inputs=[title_input, body_input, tags_input],\n",
        "                    outputs=[priority_pred, department_pred])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "KIS7lqW0uwh-"
      },
      "cell_type": "markdown",
      "source": [
        "Let's plot the model:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "IMij4gzhuzYV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "4136bee4-29fc-4852-d24d-bf2ebbfc8e19"
      },
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model, 'multi_input_and_output_model.png', show_shapes=True)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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+Pp7H4xkYGPj4+NjY2DCZTB6PN3Lk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\nnj2bl5cnOU+NtnREVA8uVqU6ora2tuLi4itXrkyZMqVPnz7yHLr9Z0n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C+Hz+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\nP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0+aNMnBwYG+MLC3t/fz87t48aK8Q3R09KJFi55+47Me4p2SkqLYLSAgwNHR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\nRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "oOyuig2Hu00p"
      },
      "cell_type": "markdown",
      "source": [
        "When compiling this model, we can assign different losses to each output.\n",
        "You can even assign different weights to each loss, to modulate their\n",
        "contribution to the total training loss."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Crtdpi5Uu2cX",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\n",
        "              loss=['binary_crossentropy', 'categorical_crossentropy'],\n",
        "              loss_weights=[1., 0.2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "t42Jrn0Yu5jL"
      },
      "cell_type": "markdown",
      "source": [
        "Since we gave names to our output layers, we could also specify the loss like this:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "dPM0EwW_u6mV",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\n",
        "              loss={'priority': 'binary_crossentropy',\n",
        "                    'department': 'categorical_crossentropy'},\n",
        "              loss_weights=[1., 0.2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "bpTx2sXnu3-W"
      },
      "cell_type": "markdown",
      "source": [
        "We can train the model by passing lists of Numpy arrays of inputs and targets:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "nB-upOoGu_k4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "85d15412-5906-493b-bc92-18002e2c266c"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Dummy input data\n",
        "title_data = np.random.randint(num_words, size=(1280, 10))\n",
        "body_data = np.random.randint(num_words, size=(1280, 100))\n",
        "tags_data = np.random.randint(2, size=(1280, num_tags)).astype('float32')\n",
        "# Dummy target data\n",
        "priority_targets = np.random.random(size=(1280, 1))\n",
        "dept_targets = np.random.randint(2, size=(1280, num_departments))\n",
        "\n",
        "model.fit({'title': title_data, 'body': body_data, 'tags': tags_data},\n",
        "          {'priority': priority_targets, 'department': dept_targets},\n",
        "          epochs=2,\n",
        "          batch_size=32)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "1280/1280 [==============================] - 11s 9ms/sample - loss: 1.2694 - priority_loss: 0.6984 - department_loss: 2.8547\n",
            "Epoch 2/2\n",
            "1280/1280 [==============================] - 11s 9ms/sample - loss: 1.2137 - priority_loss: 0.6489 - department_loss: 2.8242\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f27aaff4d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "qNguhBWuvCtz"
      },
      "cell_type": "markdown",
      "source": [
        "When calling fit with a `Dataset` object, it should yield either a\n",
        "tuple of lists like `([title_data, body_data, tags_data], [priority_targets, dept_targets])`\n",
        "or a tuple of dictionaries like\n",
        "`({'title': title_data, 'body': body_data, 'tags': tags_data}, {'priority': priority_targets, 'department': dept_targets})`.\n",
        "\n",
        "For more detailed explanation, refer to the complete guide [Guide to Training & Evaluation](./training_and_evaluation.ipynb)."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "tR0X5tTOvPyg"
      },
      "cell_type": "markdown",
      "source": [
        "### A toy resnet model\n",
        "\n",
        "In addition to models with multiple inputs and outputs,\n",
        "the Functional API makes it easy to manipulate non-linear connectivity topologies,\n",
        "that is to say, models where layers are not connected sequentially.\n",
        "This also cannot be handled with the Sequential API (as the name indicates).\n",
        "\n",
        "A common use case for this is residual connections.\n",
        "\n",
        "Let's build a toy ResNet model for CIFAR10 to demonstrate this."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "VzMoYrMNvXrm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "outputId": "95d9585d-e76f-4af3-ba09-201334cdc269"
      },
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(32, 32, 3), name='img')\n",
        "x = layers.Conv2D(32, 3, activation='relu')(inputs)\n",
        "x = layers.Conv2D(64, 3, activation='relu')(x)\n",
        "block_1_output = layers.MaxPooling2D(3)(x)\n",
        "\n",
        "x = layers.Conv2D(64, 3, activation='relu', padding='same')(block_1_output)\n",
        "x = layers.Conv2D(64, 3, activation='relu', padding='same')(x)\n",
        "block_2_output = layers.add([x, block_1_output])\n",
        "\n",
        "x = layers.Conv2D(64, 3, activation='relu', padding='same')(block_2_output)\n",
        "x = layers.Conv2D(64, 3, activation='relu', padding='same')(x)\n",
        "block_3_output = layers.add([x, block_2_output])\n",
        "\n",
        "x = layers.Conv2D(64, 3, activation='relu')(block_3_output)\n",
        "x = layers.GlobalAveragePooling2D()(x)\n",
        "x = layers.Dense(256, activation='relu')(x)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(10, activation='softmax')(x)\n",
        "\n",
        "model = keras.Model(inputs, outputs, name='toy_resnet')\n",
        "model.summary()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"toy_resnet\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to\n",
            "==================================================================================================\n",
            "img (InputLayer)                [(None, 32, 32, 3)]  0\n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 30, 30, 32)   896         img[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 28, 28, 64)   18496       conv2d_8[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 9, 9, 64)     0           conv2d_9[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 9, 9, 64)     36928       max_pooling2d_2[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 9, 9, 64)     36928       conv2d_10[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 9, 9, 64)     0           conv2d_11[0][0]\n",
            "                                                                 max_pooling2d_2[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 9, 9, 64)     36928       add[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 9, 9, 64)     36928       conv2d_12[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 9, 9, 64)     0           conv2d_13[0][0]\n",
            "                                                                 add[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 7, 7, 64)     36928       add_1[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d (Globa (None, 64)           0           conv2d_14[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "dense_9 (Dense)                 (None, 256)          16640       global_average_pooling2d[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 256)          0           dense_9[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "dense_10 (Dense)                (None, 10)           2570        dropout[0][0]\n",
            "==================================================================================================\n",
            "Total params: 223,242\n",
            "Trainable params: 223,242\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "ISQX32bgrkis"
      },
      "cell_type": "markdown",
      "source": [
        "Let's plot the model:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "pNFVkAd3rlCM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1640
        },
        "outputId": "99fd311b-7411-41b9-89d4-dd063bc6602d"
      },
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model, 'mini_resnet.png', show_shapes=True)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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KSikpKZGhQ4cKAPn888+luLhYKisrJS4uTgDIyZMnDft++OGHYm9vL1lZWVJVVSXffvut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\nFDg6CMYYY4wx9svyf5ay7t3iXwQVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "ECcG87yZrxp5"
      },
      "cell_type": "markdown",
      "source": [
        "Let's train it:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "_iXGz5XEryou",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d8a3592d-7f3c-456a-fa44-06d6c0665919"
      },
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "y_train = keras.utils.to_categorical(y_train, 10)\n",
        "y_test = keras.utils.to_categorical(y_test, 10)\n",
        "\n",
        "model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['acc'])\n",
        "model.fit(x_train, y_train,\n",
        "          batch_size=64,\n",
        "          epochs=1,\n",
        "          validation_split=0.2)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "40000/40000 [==============================] - 318s 8ms/sample - loss: 1.9034 - acc: 0.2767 - val_loss: 1.6173 - val_acc: 0.3870\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f27a93392b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "XQfg0JUkr7SH"
      },
      "cell_type": "markdown",
      "source": [
        "## Sharing layers\n",
        "\n",
        "Another good use for the functional API are models that use shared layers. Shared layers are layer instances that get reused multiple times in a same model: they learn features that correspond to multiple paths in the graph-of-layers.\n",
        "\n",
        "Shared layers are often used to encode inputs that come from similar spaces (say, two different pieces of text that feature similar vocabulary), since they enable sharing of information across these different inputs, and they make it possible to train such a model on less data. If a given word is seen in one of the inputs, that will benefit the processing of all inputs that go through the shared layer.\n",
        "\n",
        "To share a layer in the Functional API, just call the same layer instance multiple times. For instance, here's an `Embedding` layer shared across two different text inputs:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "R9pAPQCnKuMR",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Embedding for 1000 unique words mapped to 128-dimensional vectors\n",
        "shared_embedding = layers.Embedding(1000, 128)\n",
        "\n",
        "# Variable-length sequence of integers\n",
        "text_input_a = keras.Input(shape=(None,), dtype='int32')\n",
        "\n",
        "# Variable-length sequence of integers\n",
        "text_input_b = keras.Input(shape=(None,), dtype='int32')\n",
        "\n",
        "# We reuse the same layer to encode both inputs\n",
        "encoded_input_a = shared_embedding(text_input_a)\n",
        "encoded_input_b = shared_embedding(text_input_b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "xNEKvfUpr-Kf"
      },
      "cell_type": "markdown",
      "source": [
        "## Extracting and reusing nodes in the graph of layers"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "JHVGI6bEr-ze"
      },
      "cell_type": "markdown",
      "source": [
        "Because the graph of layers you are manipulating in the Functional API is a static datastructure, it can be accessed and inspected. This is how we are able to plot Functional models as images, for instance.\n",
        "\n",
        "This also means that we can access the activations of intermediate layers (\"nodes\" in the graph) and reuse them elsewhere. This is extremely useful for feature extraction, for example!\n",
        "\n",
        "Let's look at an example. This is a VGG19 model with weights pre-trained on ImageNet:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "c-gl3xHBH-oX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5584ff40-7ef9-4c98-b6fa-afe5137691ae"
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.applications import VGG19\n",
        "\n",
        "vgg19 = VGG19()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5\n",
            "574717952/574710816 [==============================] - 6s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "AKefin_xIGBP"
      },
      "cell_type": "markdown",
      "source": [
        "And these are the intermediate activations of the model, obtained by querying the graph datastructure:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "1_Ap05fgIRgE",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "features_list = [layer.output for layer in vgg19.layers]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "H1zx5qM7IYu4"
      },
      "cell_type": "markdown",
      "source": [
        "We can use these features to create a new feature-extraction model, that returns the values of the intermediate layer activations -- and we can do all of this in 3 lines."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "NrU82Pa8Igwo",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "feat_extraction_model = keras.Model(inputs=vgg19.input, outputs=features_list)\n",
        "\n",
        "img = np.random.random((1, 224, 224, 3)).astype('float32')\n",
        "extracted_features = feat_extraction_model(img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "G-e2-jNCLIqy"
      },
      "cell_type": "markdown",
      "source": [
        "This comes in handy when [implementing neural style transfer](https://medium.com/tensorflow/neural-style-transfer-creating-art-with-deep-learning-using-tf-keras-and-eager-execution-7d541ac31398), among other things."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "t9M2Uvi3sBy0"
      },
      "cell_type": "markdown",
      "source": [
        "## Extending the API by writing custom layers\n",
        "\n",
        "tf.keras has a wide range of built-in layers. Here are a few examples:\n",
        "\n",
        "- Convolutional layers: `Conv1D`, `Conv2D`, `Conv3D`, `Conv2DTranspose`, etc.\n",
        "- Pooling layers: `MaxPooling1D`, `MaxPooling2D`, `MaxPooling3D`, `AveragePooling1D`, etc.\n",
        "- RNN layers: `GRU`, `LSTM`, `ConvLSTM2D`, etc.\n",
        "- `BatchNormalization`, `Dropout`, `Embedding`, etc.\n",
        "\n",
        "If you don't find what you need, it's easy to extend the API by creating your own layers.\n",
        "\n",
        "All layers subclass the `Layer` class and implement:\n",
        "- A `call` method, that specifies the computation done by the layer.\n",
        "- A `build` method, that creates the weights of the layer (note that this is just a style convention; you could create weights in `__init__` as well).\n",
        "\n",
        "To learn more about creating layers from scratch, check out the guide [Guide to writing layers and models from scratch](./custom_layers_and_models.ipynb).\n",
        "\n",
        "Here's a simple implementation of a `Dense` layer:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ztAmarbgNV6V",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class CustomDense(layers.Layer):\n",
        "\n",
        "  def __init__(self, units=32):\n",
        "    super(CustomDense, self).__init__()\n",
        "    self.units = units\n",
        "\n",
        "  def build(self, input_shape):\n",
        "    self.w = self.add_weight(shape=(input_shape[-1], self.units),\n",
        "                             initializer='random_normal',\n",
        "                             trainable=True)\n",
        "    self.b = self.add_weight(shape=(self.units,),\n",
        "                             initializer='random_normal',\n",
        "                             trainable=True)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    return tf.matmul(inputs, self.w) + self.b\n",
        "\n",
        "inputs = keras.Input((4,))\n",
        "outputs = CustomDense(10)(inputs)\n",
        "\n",
        "model = keras.Model(inputs, outputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "NXxp_32bNWTy"
      },
      "cell_type": "markdown",
      "source": [
        "If you want your custom layer to support serialization, you should also define a `get_config` method,\n",
        "that returns the constructor arguments of the layer instance:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "K3OQ4XxzNfAZ",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class CustomDense(layers.Layer):\n",
        "\n",
        "  def __init__(self, units=32):\n",
        "    super(CustomDense, self).__init__()\n",
        "    self.units = units\n",
        "\n",
        "  def build(self, input_shape):\n",
        "    self.w = self.add_weight(shape=(input_shape[-1], self.units),\n",
        "                             initializer='random_normal',\n",
        "                             trainable=True)\n",
        "    self.b = self.add_weight(shape=(self.units,),\n",
        "                             initializer='random_normal',\n",
        "                             trainable=True)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    return tf.matmul(inputs, self.w) + self.b\n",
        "\n",
        "  def get_config(self):\n",
        "    return {'units': self.units}\n",
        "\n",
        "\n",
        "inputs = keras.Input((4,))\n",
        "outputs = CustomDense(10)(inputs)\n",
        "\n",
        "model = keras.Model(inputs, outputs)\n",
        "config = model.get_config()\n",
        "\n",
        "new_model = keras.Model.from_config(\n",
        "    config, custom_objects={'CustomDense': CustomDense})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "kXg6hZN_NfN8"
      },
      "cell_type": "markdown",
      "source": [
        "Optionally, you could also implement the classmethod `from_config(cls, config)`, which is in charge of recreating a layer instance given its config dictionary. The default implementation of `from_config` is:\n",
        "\n",
        "```python\n",
        "def from_config(cls, config):\n",
        "  return cls(**config)\n",
        "```"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "ifOVqn84sCNU"
      },
      "cell_type": "markdown",
      "source": [
        "## When to use the Functional API\n",
        "\n",
        "How to decide whether to use the Functional API to create a new model, or just subclass the `Model` class directly?\n",
        "\n",
        "In general, the Functional API is higher-level, easier & safer to use, and has a number of features that subclassed Models do not support.\n",
        "\n",
        "However, Model subclassing gives you greater flexibility when creating models that are not easily expressible as directed acyclic graphs of layers (for instance, you could not implement a Tree-RNN with the Functional API, you would have to subclass `Model` directly).\n",
        "\n",
        "\n",
        "### Here are the strengths of the Functional API:\n",
        "\n",
        "The properties listed below are all true for Sequential models as well (which are also data structures), but they aren't true for subclassed models (which are Python bytecode, not data structures).\n",
        "\n",
        "\n",
        "#### It is less verbose.\n",
        "\n",
        "No `super(MyClass, self).__init__(...)`, no `def call(self, ...):`, etc.\n",
        "\n",
        "Compare:\n",
        "\n",
        "```python\n",
        "inputs = keras.Input(shape=(32,))\n",
        "x = layers.Dense(64, activation='relu')(inputs)\n",
        "outputs = layers.Dense(10)(x)\n",
        "mlp = keras.Model(inputs, outputs)\n",
        "```\n",
        "\n",
        "With the subclassed version:\n",
        "\n",
        "```python\n",
        "class MLP(keras.Model):\n",
        "\n",
        "  def __init__(self, **kwargs):\n",
        "    super(MLP, self).__init__(**kwargs)\n",
        "    self.dense_1 = layers.Dense(64, activation='relu')\n",
        "    self.dense_2 = layers.Dense(10)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    x = self.dense_1(inputs)\n",
        "    return self.dense_2(x)\n",
        "\n",
        "# Instantiate the model.\n",
        "mlp = MLP()\n",
        "# Necessary to create the model's state.\n",
        "# The model doesn't have a state until it's called at least once.\n",
        "_ = mlp(tf.zeros((1, 32)))\n",
        "```\n",
        "\n",
        "\n",
        "#### It validates your model while you're defining it.\n",
        "\n",
        "In the Functional API, your input specification (shape and dtype) is created in advance (via `Input`), and every time you call a layer, the layer checks that the specification passed to it matches its assumptions, and it will raise a helpful error message if not.\n",
        "\n",
        "This guarantees that any model you can build with the Functional API will run. All debugging (other than convergence-related debugging) will happen statically during the model construction, and not at execution time. This is similar to typechecking in a compiler.\n",
        "\n",
        "\n",
        "#### Your Functional model is plottable and inspectable.\n",
        "\n",
        "You can plot the model as a graph, and you can easily access intermediate nodes in this graph -- for instance, to extract and reuse the activations of intermediate layers, as we saw in a previous example:\n",
        "\n",
        "```python\n",
        "features_list = [layer.output for layer in vgg19.layers]\n",
        "feat_extraction_model = keras.Model(inputs=vgg19.input, outputs=features_list)\n",
        "```\n",
        "\n",
        "\n",
        "#### Your Functional model can be serialized or cloned.\n",
        "\n",
        "Because a Functional model is a data structure rather than a piece of code, it is safely serializable and can be saved as a single file that allows you to recreate the exact same model without having access to any of the original code. See our [saving and serialization guide](./saving_and_serializing.ipynb) for more details.\n",
        "\n",
        "\n",
        "### Here are the weaknesses of the Functional API:\n",
        "\n",
        "\n",
        "#### It does not support dynamic architectures.\n",
        "\n",
        "The Functional API treats models as DAGs of layers. This is true for most deep learning architectures, but not all: for instance, recursive networks or Tree RNNs do not follow this assumption and cannot be implemented in the Functional API.\n",
        "\n",
        "\n",
        "#### Sometimes, you just need to write everything from scratch.\n",
        "\n",
        "When writing advanced achitectures, you may want to do things that are outside the scope of \"defining a DAG of layers\": for instance, you may want to expose multiple custom training and inference methods on your model instance. This requires subclassing.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "To dive more in-depth into the differences between the Functional API and Model subclassing, you can read [What are Symbolic and Imperative APIs in TensorFlow 2.0?](https://medium.com/tensorflow/what-are-symbolic-and-imperative-apis-in-tensorflow-2-0-dfccecb01021)."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Ym1jrCqusGvj"
      },
      "cell_type": "markdown",
      "source": [
        "## Mix-and-matching different API styles\n",
        "\n",
        "Importantly, choosing between the Functional API or Model subclassing isn't a binary decision that restricts you to one category of models. All models in the tf.keras API can interact with each, whether they're Sequential models, Functional models, or subclassed Models/Layers written from scratch.\n",
        "\n",
        "You can always use a Functional model or Sequential model as part of a subclassed Model/Layer:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "9zF5YTLy_vGZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e40dec06-4102-4157-9826-28a1adc78f9e"
      },
      "cell_type": "code",
      "source": [
        "units = 32\n",
        "timesteps = 10\n",
        "input_dim = 5\n",
        "\n",
        "# Define a Functional model\n",
        "inputs = keras.Input((None, units))\n",
        "x = layers.GlobalAveragePooling1D()(inputs)\n",
        "outputs = layers.Dense(1, activation='sigmoid')(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "\n",
        "class CustomRNN(layers.Layer):\n",
        "\n",
        "  def __init__(self):\n",
        "    super(CustomRNN, self).__init__()\n",
        "    self.units = units\n",
        "    self.projection_1 = layers.Dense(units=units, activation='tanh')\n",
        "    self.projection_2 = layers.Dense(units=units, activation='tanh')\n",
        "    # Our previously-defined Functional model\n",
        "    self.classifier = model\n",
        "\n",
        "  def call(self, inputs):\n",
        "    outputs = []\n",
        "    state = tf.zeros(shape=(inputs.shape[0], self.units))\n",
        "    for t in range(inputs.shape[1]):\n",
        "      x = inputs[:, t, :]\n",
        "      h = self.projection_1(x)\n",
        "      y = h + self.projection_2(state)\n",
        "      state = y\n",
        "      outputs.append(y)\n",
        "    features = tf.stack(outputs, axis=1)\n",
        "    print(features.shape)\n",
        "    return self.classifier(features)\n",
        "\n",
        "rnn_model = CustomRNN()\n",
        "_ = rnn_model(tf.zeros((1, timesteps, input_dim)))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 10, 32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "oxW1d0a8_ufg"
      },
      "cell_type": "markdown",
      "source": [
        "Inversely, you can use any subclassed Layer or Model in the Functional API as long as it implements a `call` method that follows one of the following patterns:\n",
        "\n",
        "- `call(self, inputs, **kwargs)`  where `inputs` is a tensor or a nested structure of tensors (e.g. a list of tensors), and where `**kwargs` are non-tensor arguments (non-inputs).\n",
        "- `call(self, inputs, training=None, **kwargs)` where `training` is a boolean indicating whether the layer should behave in training mode and inference mode.\n",
        "- `call(self, inputs, mask=None, **kwargs)` where `mask` is a boolean mask tensor (useful for RNNs, for instance).\n",
        "- `call(self, inputs, training=None, mask=None, **kwargs)` -- of course you can have both masking and training-specific behavior at the same time.\n",
        "\n",
        "In addition, if you implement the `get_config` method on your custom Layer or Model, the Functional models you create with it will still be serializable and clonable.\n",
        "\n",
        "Here's a quick example where we use a custom RNN written from scratch in a Functional model:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "TmTEZ6F3ArJR",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "units = 32\n",
        "timesteps = 10\n",
        "input_dim = 5\n",
        "batch_size = 16\n",
        "\n",
        "\n",
        "class CustomRNN(layers.Layer):\n",
        "\n",
        "  def __init__(self):\n",
        "    super(CustomRNN, self).__init__()\n",
        "    self.units = units\n",
        "    self.projection_1 = layers.Dense(units=units, activation='tanh')\n",
        "    self.projection_2 = layers.Dense(units=units, activation='tanh')\n",
        "    self.classifier = layers.Dense(1, activation='sigmoid')\n",
        "\n",
        "  def call(self, inputs):\n",
        "    outputs = []\n",
        "    state = tf.zeros(shape=(inputs.shape[0], self.units))\n",
        "    for t in range(inputs.shape[1]):\n",
        "      x = inputs[:, t, :]\n",
        "      h = self.projection_1(x)\n",
        "      y = h + self.projection_2(state)\n",
        "      state = y\n",
        "      outputs.append(y)\n",
        "    features = tf.stack(outputs, axis=1)\n",
        "    return self.classifier(features)\n",
        "\n",
        "# Note that we specify a static batch size for the inputs with the `batch_shape`\n",
        "# arg, because the inner computation of `CustomRNN` requires a static batch size\n",
        "# (when we create the `state` zeros tensor).\n",
        "inputs = keras.Input(batch_shape=(batch_size, timesteps, input_dim))\n",
        "x = layers.Conv1D(32, 3)(inputs)\n",
        "outputs = CustomRNN()(x)\n",
        "\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "rnn_model = CustomRNN()\n",
        "_ = rnn_model(tf.zeros((1, 10, 5)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "6VxcYb4qArlb"
      },
      "cell_type": "markdown",
      "source": [
        "This concludes our guide on the Functional API!\n",
        "\n",
        "Now you have at your fingertips a powerful set of tools for building deep learning models."
      ]
    }
  ]
}

